Results 11 to 20 of about 18,144,236 (296)
Wiley StatsRef: Statistics Reference Online, 2022 We supplement the article of Meng (2006) on the EM algorithm and its applications, providing also an update on its more recent developments and applications.
Geoffrey J. McLachlan +2 more
semanticscholar +2 more sources
flexCWM: A Flexible Framework for Cluster-Weighted Models
Journal of Statistical Software, 2018 Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical ...
Angelo Mazza +2 more
doaj +1 more source
Variable selection in finite mixture of median regression models using skew-normal distribution
Statistical Theory and Related Fields, 2023 A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.
Xin Zeng, Yuanyuan Ju, Liucang Wu
doaj +1 more source
Hierarchical Mixtures of Experts and the EM Algorithm
Neural Computation, 1993 We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's).
M. I. Jordan, R. Jacobs
semanticscholar +1 more source
logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model
Journal of Statistical Software, 2018 Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysis of binary outcomes. However, Fisher scoring, which is the standard method for fitting GLMs in statistical software, may have ...
Mark W. Donoghoe, Ian C. Marschner
doaj +1 more source
Determination of Load Equivalency Factors by Statistical Analysis of Weigh-In-Motion Data
The Baltic Journal of Road and Bridge Engineering, 2016 The load equivalency factors for pavement design currently in use by the Hungarian standard have been developed using Weigh-in-Motion data obtained during the first few years of operations after installing some 30 measuring sites in Hungary in 1996.
Zoltán Soós, Csaba Tóth, Dávid Bóka
doaj +1 more source
Statistical convergence of the EM algorithm on Gaussian mixture models [PDF]
Electronic Journal of Statistics, 2018 We study the convergence behavior of the Expectation Maximization (EM) algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by at least $\
Ruofei Zhao, Yuanzhi Li, Yuekai Sun
semanticscholar +1 more source
SGA based symbol detection and EM channel estimation for MIMO systems [PDF]
, 2006 This paper investigates iterative channel estimation and symbol detection for spatial multiplexing multiple input multiple output (MIMO) systems with frequency flat block fading channels using the expectation-maximization (EM) algorithm.
Jia, Yugang +3 more
core +1 more source
Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy
Journal of Intelligent Systems, 2019 In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated.
Resmi V, Vijayalakshmi S
doaj +1 more source
Estimating parameters of factor analysis model maximum likelihood method)) by using EM algorithm with application [PDF]
مجلة التربية والعلم, 2009 Expectation maximization algorithm (EM) is used to create estimator with the same qualities of maximum likelihood Estimator taking into consideration the existence of two types of data, Data viewing (observed data) and hidden data (missing data), in this
Thanoon alshakerchy
doaj +1 more source